Methods for estimating the seasonality of groups of similar items of commerce data sets based on historical sales data values and associated error information

ABSTRACT

A set of data is received containing values associated with respective data points, the values associated with each of the data points being characterized by a distribution. The values for each of the data points are expressed in a form that includes information about a distribution of the values for each of the data points. The distribution information is used in clustering the set of data with at least one other set of data containing values associated with data points.

BACKGROUND

This invention relates to clustering, for example, of seasonalities tobetter forecast demand for items of commerce.

Clustering means grouping objects, each of which is defined by values ofattributes, so that similar objects belong to the same cluster anddissimilar objects belong to different clusters. Clustering hasapplications in many fields, including medicine, astronomy, marketing,and finance.

Clustering is done on the assumption that attribute values representingeach object to be clustered are known deterministically with no errors.Yet, often, the values representing an object to be clustered are notavailable. Sometimes statistical methods are used to get estimated oraverage values for a given object.

SUMMARY

In general, in one aspect, the invention features a method that includes(a) receiving a set of data containing values associated with respectivedata points, the values associated with each of the data points beingcharacterized by a distribution, (b) expressing the values for each ofthe data points in a form that includes information about a distributionof the values for each of the data points, and (c) using thedistribution information in clustering the set of data with at least oneother set of data containing values associated with data points.

Implementations of the invention may include one or more of thefollowing features. The respective data points are related in atime-sequence. The data points relate to a seasonality of at least oneitem of commerce. Each of the sets of data relates to seasonalities ofitems of commerce. The items of commerce comprise retail products, thedata points relate to times during a season, and the values associatedwith each of the data points correspond to respective ones of the retailproducts. The method also includes determining statistical measures ofthe variability of the values with respect to the data point. The datais expressed in a form that includes a mean of the values associatedwith a data point and a statistical measure of the distribution withrespect to the mean. The statistical measure comprises a standarddeviation. The clustering of data includes measuring a distance betweenpairs of the sets of data. The distance is measured based on the meansand variances at the data points. The distribution of the values isGaussian. The clustering of data includes merging the data setsbelonging to a cluster using a weighted average. The method includesmerging the seasonalities of the data sets belong to a cluster.

In general, in another aspect, the invention features amachine-accessible medium that when accessed results in a machineperforming operations that include: (a) receiving a set of datacontaining values associated with respective data points, the valuesassociated with each of the data points being characterized by adistribution, (b) expressing the values for each of the data points in aform that includes information about a distribution of the values foreach of the data points, and (c) using the distribution information inclustering the set of data with at least one other set of datacontaining values associated with data points.

In general, in another aspect, the invention features a method thatincludes (a) receiving sets of data, each of the sets containing valuesassociated with respect data points, the values associated with each ofthe data points being characterized by a distribution, (b) evaluating adistance function that characterizing the similarity or dissimilarity ofat least two of the sets of data, the distance function including afactor based on the distributions of the values in the sets, and (c)using the evaluation of the distance function as a basis for clusteringof the sets of data.

In general, in another aspect, the invention features a method thatincludes (a) receiving data that represents seasonality time-series foreach of a set of retail items, the data also representing errorinformation associated with data values for each of a series of timepoints for each of the items, and (b) forming composite seasonalitytime-series based on respective clusters of the retail item seasonalitytime-series, the composites formed based in part on the errorinformation.

Other advantages and features will become apparent from the followingdescription and from the claims.

DESCRIPTION

(FIG. 1 shows product life cycle curves.

FIG. 2 shows a seasonality curve and two sales curves.

FIG. 3 shows a concatenation of PLC curves and an average curve.

FIG. 4 shows three seasonality curves.

FIG. 5 illustrates the merger of time-series.

FIG. 6 illustrates the merger of time-series.

FIGS. 7, 8, 9, 10 and 11 show seasonality curves.

FIG. 12 is a flow chart.)

INTRODUCTION

The clustering of objects can be improved by incorporating informationabout errors, which are a measure of level of confidence, in data thatcharacterize the objects to be clustered. Instead of relying on simpleaverage values for attributes of the objects, measures of error such asstandard deviation or an estimate of an entire distribution of the datafor a value can be used to improve the quality of the clustering. A newdistance function that is based on the distribution of errors in data(and may be viewed as a generalization of the classical Euclidiandistance function) can be used in clustering.

Although the following discussion focuses on the utility of the newdistance function for time-series clustering, the concept ofincorporating information about error in the distance function can beused in developing a distance function in any clustering application inwhich data that is clustered in other ways.

The Problem

We describe the problem being solved in the following new way:

Let m objects (e.g., seasonalities) be represented by m data points(e.g., sales demand rates) in n-dimensional space (e.g., representing nweeks). Each of these m data points represents a Gaussian distributionof data values, which defines the average value of each data point andalso specifies the standard error associated with each data point.

We group these data points into clusters so that it is likely that datapoints (sales demand rates) in the same cluster are similar to (closeto) each other, and data points in different clusters are unlikely to beclose to each other, the likelihood being defined with respect to theGaussian distributions represented by the data points. In contrast toother clustering techniques, two data points that differ significantlyin their means may belong to the same cluster if they have high errorsassociated with them, and two data points that do not differ much intheir means might belong to different clusters if they arewell-calculated and have small errors.

Seasonality Forecasting

As one example, we consider the forecasting of seasonality for retailersbased on sales data from previous years. Seasonality is defined as theunderlying demand for a group of similar merchandise items as a functionof time of the year that is independent of external factors like changesin price, inventory, and promotions. Seasonality for an item is expectedto behave consistently from year to year.

In the retail industry, it is important to understand the seasonalbehaviors in the sales of items to correctly forecast demand and makeappropriate business decisions with respect to each item. Differentitems may be characterized by different seasonalities for a year. Yet,to reduce random variability and make better demand forecasts, it isdesirable to group items with similar seasonal behavior together.

In this application, the objects to be clustered are the seasonalitiesof different groups of retail items. Effective clustering of retailitems yields better forecasting of seasonal behavior for each of theretail items. Therefore, we identify a set of clusters of seasonalitiesthat model most of the items sold by the retailer and relate each itemto average seasonality of the cluster.

We model seasonality forecasting as a time-series clustering problem inthe presence of errors, and we discuss experimental results on retailindustry data. We have discovered meaningful clusters of seasonalitythat have not been uncovered by clustering methods that do not use theerror information.

We assume that external factors, like price, promotions, and inventory,have been filtered from our data, and that the data has been normalizedso as to compare sales of different items on the same scale. Afternormalization and filtering for external factors, the remaining demandrate of an item is determined by its Product Life Cycle (PLC) andseasonality. PLC is defined as the base demand of an item over time inthe absence of seasonality and all other external factors.

Filtering Product Life Cycle Effects

As shown in FIG. 1, for example, in a typical PLC 14, an item isintroduced on a certain date 10 and removed from stores on a certaindate 12. PLC 14 is a curve between the introduction date and removaldate. The shape of the curve is determined by the duration of time anitem is sold 16 and also the nature of the item. For example, a fashionitem (right-hand curve 15) will sell out faster a non-fashion item(left-hand curve 14). For simplicity, we assume the PLC value to be zeroduring weeks when the item is not sold.

Because sales of an item is a product of its PLC and its seasonality, itis not possible to determine seasonality just by looking at the salesdata of an item. The fact that items having the same seasonality mighthave different PLCs complicates the problem.

For example, if both the items for which PLC curves are shown in FIG. 1follow the same seasonality 20 as shown in FIG. 2, then the sales of thetwo items (non-fashion and fashion) will be as shown in the twodifferent curves 22 and 24 shown in the right-hand side of FIG. 2.Curves 22 and 24 are respectively the products of curves 14, 15 of FIG.1 and the seasonality curve 20 in FIG. 2.

The first step is to remove as much as possible of the PLC factor fromthe sales data so that only the seasonality factor remains. Initially,based on prior knowledge from merchants, we group items that arebelieved to follow similar seasonality over an entire year. As shown inFIG. 3, the items in the set 30 follow similar seasonality but may beintroduced and removed at different points of time during the year. Theset typically includes items having a variety of PLCs that differ intheir shapes and durations of time. The weekly average of all PLCs inthis set is a somewhat flat curve 32 as shown on the right-hand side ofFIG. 3, implying that the weekly average of PLCs for all items in theset can be assumed to be constant.

This implies that averaging of weekly filtered demand rates of all itemsin the set would nullify the effect of PLCs and would correspond to thecommon value of seasonality indices for the items in the set to within aconstant factor. The constant factor can be removed by appropriatescaling of weekly sales averages. Although the average values obtainedabove give a reasonably good estimate of seasonality, they will haveerrors associated with them depending on how many items were used toestimate seasonality and also on how well spread are their PLCs.

Error Information

Let σ_(j) ², be the variance of all the filtered demand rates for a set(e.g., one of the sets described above) of retail items in week j. To beclear, we are capturing the variance of the data values that representthe units of sales of the various items for a given week j as found in,say, one year of historical sales data. If there are a total of m itemsin this set, then an estimate of s_(j), which is the standard error inthe estimation of the seasonality index for week j, is given by thefollowing equation. $\begin{matrix}{S_{j} = \frac{\sigma_{j}}{\sqrt{m}}} & (1)\end{matrix}$

By seasonality index for week j, we mean the factor that represents theseasonality effect for all m items in the set relative to the baselinedemand value. So, if the baseline demand is 20 units in week j and theseasonality index in week j is 1.3 then we expect the sales data in weekj to be 26 units.

The above procedure provides a large number of seasonal indices, one foreach set of retail items of merchandise, along with estimates ofassociated errors. We group these seasonal indices into a few clustersbased on the average values and errors as calculated above, thusassociating each item with one of these clusters of seasonal indices.The seasonality index of a cluster is defined as a weighted average ofseasonalities present in the cluster as shown by equation 3 below. Thecluster seasonality obtained in this way is much more robust because ofthe large number of seasonalities used to estimate it.

To summarize the discussion to this point, based on prior knowledgeabout which items, of the thousands of items that appear in a merchant'shierarchy of items, follow similar seasonality, we group items into setsof items that have similar seasonality. We estimate the seasonality foreach of these sets and also estimate the error associated with ourestimate of seasonality. This provides us with a large number ofseasonalities along with associated errors. We cluster theseseasonalities into a few clusters, then calculate the averageseasonality of each cluster. The final seasonality of an item is theseasonality of the cluster to which it belongs.

Generating seasonality forecasts by clustering without incorporating theerrors information would disregard the fact that we do not know how muchconfidence we have in each seasonality. If we have high confidence inthe estimate of a seasonality, then we would like to be more careful inassigning it to a cluster. On the other hand, if we have littleconfidence in the estimate of a seasonality, then its membership in acluster does not have high significance in the cumulative averageseasonality of the cluster. Errors or the associated probabilitydistributions capture the confidence level of each seasonality and canbe used intelligently to discover better clusters in the case ofstochastic data.

Representation of Time-Series

Time-series are widely used in representing data in business, medical,engineering, and social sciences databases. Time-series data differsfrom other data representations in the sense that data points in atime-series are represented by a sequence typically measured at equaltime intervals.

Stochastic data can be represented by a time-series in a way that modelserrors associated with data.

A time-series of stochastic data sampled at k weeks is represented by asequence of k values. In our application, where we assume, for example,that the k samples are independent of each other and are eachdistributed according to one-dimensional Gaussian distribution, werepresent a time-series A as:

A={(μ₁,s₁,w₁),(μ₂,s₂,w₂), . . . ,(μ_(k),s_(k),w_(k))}

where the stochastic data of the i^(th) sample of A is normallydistributed with mean μ_(i) and standard deviation s_(i). w_(i) is aweight function to give relative importance to each sample of thetime-series. Weights are chosen such that${\sum\limits_{i}\left( {\mu_{i}*w_{i}} \right)} = k$

so as to express time-series on the same scale. This normalization isimportant in the following sense. First of all, these weights reflectthe relative, not absolute, importance of each sampled value. Secondly,the normalization converts the data into a unit scale thereby comparingdifferences in the shapes of time-series and not in the actual values.In FIG. 4, for example, the normalization will facilitate putting thetime-series A and the time-series B in the same cluster and thetime-series C in a separate cluster.

Although one can experiment with different weights for different samplevalues of a time-series, for simplicity, we assume that all the k samplevalues of a time-series have equal weight. Henceforth, we will work withthe following compact representation of time-series.

A={(μ′₁,s′₁),(μ′₂,s′₂), . . . ,(μ′_(k),s′_(k))},

where μ′_(i)=μ_(i)*w and${s_{i}^{\prime} = {s_{i}^{*}w}},\quad {{{for}\quad i} = 1},2,{{\ldots \quad k\quad {where}\quad w} = {\frac{k}{\sum\limits_{i}\mu_{i}}.}}$

In the case of seasonality forecasting, we may have k equal to 52corresponding to 52 weeks in a year, and μ_(i) be the estimate of theseasonality index for i^(th) week. s_(i) represents the standard errorin the estimated value of μ_(i).

We have assumed that sales data that are used to estimate means anderrors of the respective k sample values of a time-series come fromindependent distributions. Although we might observe some level ofindependence, complete independence is not possible in real data.Especially in time-series data, one expects a positive correlation inconsecutive sample values. Therefore, while incorporating the concept ofdependence can be difficult, it can improve the test statistic fordistance and subsequently give a more accurate measure of the distancefunction. In the case of seasonality forecasting, we deal withseasonality values that are obtained by taking the average of sales dataof items having different PLCs. These PLCs are pretty much random for alarge sample of data and therefore averaging over these random PLCs maydampen the effect of dependency among different samples. This impliesthat dependency is not a serious issue in our application, a propositionthat is also observed experimentally.

Distance Function

As in other clustering methods, we make a basic assumption that therelationship among pairs of seasonalities in a set of n seasonalities isdescribed by an n×n matrix containing a measure of dissimilarity betweenthe i^(th) and the j^(th) seasonalities. In clustering parlance, themeasure of dissimilarity is referred to as a distance function betweenthe pair of seasonalities. Various distance functions have beenconsidered for the case of deterministic data. We have developed aprobability-based distance function in the case of multidimensionalstochastic data.

Consider two estimated seasonality indices

A_(i)={(μ_(i1),s_(i1)),(μ_(i2),s_(i2)), . . . ,(μ_(ik),s_(ik))}

and

A_(j)={(μ_(j1),s_(j1)),(μ_(j2),s_(j2)), . . . ,(μ_(jk),s_(jk)).

A_(i) and A_(j) are the estimated time-series of two seasonalities basedon historically observed sales for corresponding sets of items. Let thecorresponding true seasonalities be

{{overscore (μ)}_(i1),{overscore (μ)}_(i2), . . . ,{overscore (μ)}_(ik)}

and

{{overscore (μ)}_(i1),{overscore (μ)}_(i2), . . . ,{overscore (μ)}_(ik)}

This means that the μ's are the observed means that are associated withthe true means of {overscore (μ)}'s.

We define similarity between two seasonalities (time-series) as theprobability that the two seasonalities might be the same. Twoseasonalities are considered the same if the corresponding μ's are closewith high significance with respect to the associated errors. In otherwords, if we define the null hypothesis H₀ as A_(i)=A_(j) thensimilarity between A_(i) and A_(j) is the significance level of thishypothesis. Here, A_(i)=A_(j) means corresponding {overscore(μ)}_(i1)={overscore (μ)}_(j1) for l=1, . . . , k. The distance ordissimilarity d_(ij) between A_(i) and A_(j) is defined as(1−similarity). In other words, d_(ij) is the probability of rejectingthe above hypothesis. This distance function satisfies the followingdesirable properties.

dist(A,B)=dist(B,A)

dist(A,B)≧0

 dist(A,A)=0

dist(A,B)=0→A=B

The statistic for the test of the above hypothesis is$\sum\limits_{l = 1}^{k}\left( \frac{\mu_{i\quad l} - \mu_{j\quad l}}{s_{l}} \right)^{2}$

where s₁ is the pooled variance for week l defined as √{square root over(s_(il) ²+s_(jl) ²)}

Under a Gaussian assumption, the above statistic follows a Chi-Squaredistribution with k−1 degrees of freedom. Therefore, the distance d_(ij)which is the significance level of rejecting the above hypothesis isgiven by the following equation. $\begin{matrix}{d_{i\quad j} = {{ChiSqr\_ PDF}\left( {{\sum\limits_{l = 1}^{k}\left( \frac{\mu_{i\quad l} - \mu_{j\quad l}}{s_{l}} \right)^{2}},{k - 1}} \right)}} & (2)\end{matrix}$

We use this distance between pairs of seasonality indices as the basisfor clustering them by putting pairs that have low distances in the samecluster and pairs that have high distances in different clusters. We usea confidence interval (e.g., 90%) to find a threshold distance. If thedistance between two seasonalities is less than the threshold distancevalue, we put them in the same cluster.

Merging Time-series

Here we define a ‘merge’ operation to combine information from a set oftime-series and produce a new time-series that is a compromise betweenall the time-series used to produce it. In seasonality forecasting, thetime-series are sets of seasonalities and the new time-series representsthe weighted average seasonality for a cluster of seasonalities. Theshape of the resulting time-series depends not only on the sample valuesof individual time-series but also on errors associated with individualtime-series.

Given r time-series

A_(i)={(μ_(i1),s_(i1)),(μ_(i2),s_(i2)), . . . ,(μ_(ik),s_(ik))},

i=1,2, . . . ,r then the resulting time-series

C={(μ₁,s₁),(μ₂,s₂), . . . ,(μ_(k),s_(k))}

is given by $\begin{matrix}{{\mu_{j} = {{\frac{\sum\limits_{i = 1}^{r}\frac{\mu_{i\quad j}}{s_{\quad {i\quad j}}^{2}}}{\sum\limits_{i = 1}^{r}\frac{1}{s_{i\quad j}^{2}}}\quad j} = 1}},2,\ldots \quad,k} & (3) \\{{s_{j} = {{\frac{1}{\sqrt{\sum\limits_{i = 1}^{r}\frac{1}{s_{i\quad j}^{2}}}}\quad j} = 1}},2,\ldots \quad,k} & (4)\end{matrix}$

As shown in the example of FIG. 5, consider two time-series 40 and 42 inwhich the curves 44, 46 represent the average values at each of 26samples of time represented along the horizontal axis. The vertical line48 for each of the samples represents the error associated with thesample at that time. The time-series 50 is the resulting sequence whenthe time-series 40 and 42 are merged in the manner discussed above. Ascan be seen, portions of each of the time-series 40 and 42 for which theerrors are relatively smaller than in the other time-series serve moreprominently in the merged time series 50. Therefore, if the series 50represented a merged seasonality of two merchandise items as part of acluster of items, a retailer could use the series 50 as a more accurateprediction of seasonality of the items that make up the cluster thanwould have been represented by clustered seasonalities that were mergedwithout the benefit of the error information. This helps the retailermake better demand forecast as shown by experimental results.

Experimental Results

We generated data sets as described below. First we generated tendifferent kinds of PLCs from a Weibull distribution with differentparameters. These PLCs differ in their peaks and shapes depending on theparameters used in the distribution, as shown in FIG. 6. The PLC data israndomly generated by choosing one of these 10 PLCs with equalprobability and a uniformly distributed starting time over a period ofone year.

Then we considered three different seasonalities corresponding toChristmas seasonality, summer seasonality, and winter seasonalityrespectively, as shown in FIG. 7. We generated sales data by multiplyingthe randomly generated PLC data with one of the three seasonalities.When we considered twelve instances, each instance produced bygenerating 25-35 PLCs, multiplying them by one of the aboveseasonalities and averaging weekly sales to obtain an estimate ofcorresponding seasonality, we obtained the values of seasonalities withassociated estimates of errors as shown in FIG. 8.

As shown in FIG. 8, some of the seasonalities do not correspond to anyof the original seasonalities and each has large errors. We ran theclustering method as described above and we got three clusters withcluster centers as shown in FIG. 9, where cluster centers are obtainedby averaging of all PLCs in the same cluster according to equations 3and 4. The resulting seasonalities match the original seasonalities ofFIG. 7 well as shown in FIG. 9. We compared our result with standardhierarchical clustering that did not consider the information abouterrors. The number of misclassifications were higher when we usedhierarchical clustering with standard Euclidean distance withoutaccounting for errors; as shown in FIG. 10.

Actual Data Results

FIG. 11 shows an example based on actual retail sales data. Each of theseven time-series in this figure represents a seasonality that isobtained from a group of items that are known to follow similarseasonality. The clustering of the seven sets provides five clusters asshown in the figure.

The expressing of data in a way that incorporates error information, theexpressing of a distance function based on the error information, theclustering of data sets using the distance function, the merging ofclusters of data sets, the specific applications of the techniques totime-series data, including seasonality time-series for retail items,and other techniques described above, can be done using computers ormachines controlled by software instructions.

For example, as shown in FIG. 12, the software would be stored in memoryor on a medium or made available electronically, and would enable thecomputer or machine to perform the following sequence, for example, inthe context of a retailer making pricing or inventory decisions withrespect to retail items. After filtering and normalizing the historicaltime-sequence sales data for a number of items, in step 80, the datawould be grouped and processed in step 82 to reduce the effect of theproduct life cycle factor. This would include grouping items known tohave similar seasonality, scaling, and determining variance and standarderrors with respect to each group. The seasonality indices for each ofthe groups would then be expressed in a representation (step 84) thatcaptures both (a) the mean values of seasonality for a set of items foreach period and (b) the statistical or error information that representsthe confidence level with respect to the mean values of seasonality. Thesoftware would analyze the various seasonality time-series using thedistance functions (step 86) to cluster the time-series, and then woulduse the clustered time-series as part of the basis for decision-making(step 88). Additional information about the collection of suchinformation and the making of such decisions is found in U.S. patentapplications Ser. No. 09/263,979, filed Mar. 5, 1999, Ser. No.09/826,378, filed Apr. 4, 2001, and Ser. No. 09/900,706, filed Jul. 6,2001, all incorporated by reference here.

Other implementations are within the scope of the following claims.

What is claimed is:
 1. A computer-based method of estimating aseasonality of clusters of similar items of commerce based on historicalsales data values and associated error information comprising: executingon a computer the steps (A)-(E) as follows: A. receiving a first set ofdata containing a plurality of data points, each of which data pointsrepresent historical sales data for one or more of the items of commerceand which is expressed as a value and an associated error, B.determining a distance between the first set of data and each of one ormore other sets of data, where each of the other data sets contains aplurality of data points, each of which data points representshistorical sales data for one or more of the items of commerce and whichis expressed as a value and an associated error, C. the determining stepincluding measuring each distance based on the relation:$d_{i\quad j} = {{ChiSqr\_ PDF}\left( {{\sum\limits_{l = 1}^{k}\left( \frac{\mu_{i\quad l} - \mu_{j\quad l}}{s_{l}} \right)^{2}},{k - 1}} \right)}$

where  d_(ij) is a measure of the distance between set of data i and ofdata j  ChiSqr_PDF is a Chi-square distribution function  k is a numberof data points in each of sets of data i and j  l is an index  μ_(il) isa l^(th) value of set of data i  μjl is a l^(th) value of set of data j s={square root over (s_(il) ²+s_(jl) ²)}, where s_(il) is errorassociated with the l^(th) value of set of data i and s_(ij) is theerror associated with the l^(th) value of set of data j. D. clusteringthe first set of data with at least one of the other sets of data, theclustering step including comparing with a threshold one or moredistances determined in the determining step, E. generating a compositedata set that estimates the seasonality of one or more clusters of theitems of commerce, where the composite data set is based on clusteringperformed in step (D) and is generated as a function of data pointscontained in the first set of data and the one or more other sets ofdata, if any, whose distances compared favorably with the threshold. 2.The method of claim 1 in which at least one value and the at least onevalue's associated error comprise a Gaussian distribution.
 3. The methodof claim 1 in which the clustering step includes merging using aweighted average data sets whose distances compared favorably with thethreshold.
 4. The method of claim 1, wherein the clustering stepincludes merging seasonalities contained in data sets belonging to acluster.
 5. A computer-based method of estimating a seasonality ofclusters of similar items of commerce based on historical sales datavalues and associated error information comprising executing on acomputer steps (A)-(C) as follows A. determining distances betweenrespective pairs of data sets in a plurality of data sets, where eachdata set contains a plurality of data points forming a time-series ofretail data, where each data point is expressed as a value and anassociated error, B. the determining step including measuring eachdistance as a function of the relation$d_{i\quad j} = {{ChiSqr\_ PDF}\left( {{\sum\limits_{l = 1}^{k}\left( \frac{\mu_{i\quad l} - \mu_{j\quad l}}{s_{l}} \right)^{2}},{k - 1}} \right)}$

where  d_(ij) is a measure of the distance between set of data i and setof data j  Chisqr_PDF is a Chi-square distribution function  k is anumber of data points in/each of sets of data i and j  l is an index μ_(u) is a l^(th) value of set of data i  μ_(jl) is a l^(th) value ofset of data j  s=√{square root over (s_(il) ²+s_(jl) ²)}, where s_(il)is error associated with the l^(th) value of set of data i and s_(jl) isthe error associated with the l^(th) value of set of data j, and C.estimating the seasonality of one or more clusters of the items ofcommerce based on clustering the plurality of data sets as a function ofthe distances determined in the determining step, the clustering stepincluding forming one or more composite data sets, each containing aplurality of data points forming a time-series of retail data based ondata points contained in data sets whose distances compared favorablywith a threshold.
 6. The method of claim 5, wherein the retail data issales demand data.
 7. The method of claim 6, wherein the retail data isseasonality data for items of commerce.
 8. The method of claim 5,wherein the sets of data contain a plurality of data points forming atime-series of sales demand data, the method further comprising the stepof processing one or more of the sets of data to filter factors otherthan seasonality, where seasonality is defined as the underlying demandfor a group of merchandise items as a function of time of the year. 9.The method of claim 8, wherein the processing step includes filteringthe one or more set of data to filter product life cycle effects. 10.The method of claim 9, wherein the processing filtering step includes atleast one of averaging and scaling data points in one or more of thesets.